


'''simple docstring'''






import argparse
import logging
import os
import sys

import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers

import transformers
from transformers import BartForConditionalGeneration, BartTokenizer


logging.basicConfig(
    format='''%(asctime)s | %(levelname)s | %(name)s |  [%(filename)s:%(lineno)d] %(message)s''',
    datefmt='''%Y-%m-%d %H:%M:%S''',
    level=os.environ.get('''LOGLEVEL''', '''INFO''').upper(),
    stream=sys.stdout,
)

__UpperCAmelCase       							=				logging.getLogger(__name__)

__UpperCAmelCase       							=				{'''facebook/bart-base''': BartForConditionalGeneration}
__UpperCAmelCase       							=				{'''facebook/bart-base''': BartTokenizer}


def    _snake_case  (	)		->       Any:
							lowerCAmelCase__			  =      argparse.ArgumentParser(description='''Export Bart model + Beam Search to ONNX graph.'''			)
							parser.add_argument(
							    '''--validation_file'''					,       type=A					,       default=A					,       help='''A csv or a json file containing the validation data.'''			)
							parser.add_argument(
							    '''--max_length'''					,       type=A					,       default=5					,       help='''The maximum total input sequence length after tokenization.'''					,       )
							parser.add_argument(
							    '''--num_beams'''					,       type=A					,       default=A					,       help=(
							        '''Number of beams to use for evaluation. This argument will be '''
							        '''passed to ``model.generate``, which is used during ``evaluate`` and ``predict``.'''
							    )					,       )
							parser.add_argument(
							    '''--model_name_or_path'''					,       type=A					,       help='''Path to pretrained model or model identifier from huggingface.co/models.'''					,       required=A					,       )
							parser.add_argument(
							    '''--config_name'''					,       type=A					,       default=A					,       help='''Pretrained config name or path if not the same as model_name'''					,       )
							parser.add_argument(
							    '''--device'''					,       type=A					,       default='''cpu'''					,       help='''Device where the model will be run'''					,       )
							parser.add_argument('''--output_file_path'''					,       type=A					,       default=A					,       help='''Where to store the final ONNX file.'''			)

							lowerCAmelCase__			  =      parser.parse_args()

							return args


def    _snake_case  (	A					,       A="cpu"			)		->       int:
							lowerCAmelCase__			  =      model_dict[model_name].from_pretrained(A			).to(A			)
							lowerCAmelCase__			  =      tokenizer_dict[model_name].from_pretrained(A			)

							if model_name in ["facebook/bart-base"]:
														lowerCAmelCase__			  =      0
														lowerCAmelCase__			  =      None
														lowerCAmelCase__			  =      0

							return huggingface_model, tokenizer


def    _snake_case  (	A					,       A					,       A					,       A					,       A			)		->       Union[str, Any]:
							model.eval()

							lowerCAmelCase__			  =      None
							lowerCAmelCase__			  =      torch.jit.script(BARTBeamSearchGenerator(A			)			)

							with torch.no_grad():
														lowerCAmelCase__			  =      '''My friends are cool but they eat too many carbs.'''
														lowerCAmelCase__			  =      tokenizer([ARTICLE_TO_SUMMARIZE]					,       max_length=1024					,       return_tensors='''pt'''			).to(model.device			)

														lowerCAmelCase__			  =      model.generate(
														    inputs['''input_ids''']					,       attention_mask=inputs['''attention_mask''']					,       num_beams=A					,       max_length=A					,       early_stopping=A					,       decoder_start_token_id=model.config.decoder_start_token_id					,       )

														torch.onnx.export(
														    A					,       (
														        inputs['''input_ids'''],
														        inputs['''attention_mask'''],
														        num_beams,
														        max_length,
														        model.config.decoder_start_token_id,
														    )					,       A					,       opset_version=14					,       input_names=['''input_ids''', '''attention_mask''', '''num_beams''', '''max_length''', '''decoder_start_token_id''']					,       output_names=['''output_ids''']					,       dynamic_axes={
														        '''input_ids''': {0: '''batch''', 1: '''seq'''},
														        '''output_ids''': {0: '''batch''', 1: '''seq_out'''},
														    }					,       example_outputs=A					,       )

														logger.info('''Model exported to {}'''.format(A			)			)

														lowerCAmelCase__			  =      remove_dup_initializers(os.path.abspath(A			)			)

														logger.info('''Deduplicated and optimized model written to {}'''.format(A			)			)

														lowerCAmelCase__			  =      onnxruntime.InferenceSession(A			)
														lowerCAmelCase__			  =      ort_sess.run(
														    A					,       {
														        '''input_ids''': inputs['''input_ids'''].cpu().numpy(),
														        '''attention_mask''': inputs['''attention_mask'''].cpu().numpy(),
														        '''num_beams''': np.array(A			),
														        '''max_length''': np.array(A			),
														        '''decoder_start_token_id''': np.array(model.config.decoder_start_token_id			),
														    }					,       )

														np.testing.assert_allclose(summary_ids.cpu().numpy()					,       ort_out[0]					,       rtol=1E-3					,       atol=1E-3			)

														logger.info('''Model outputs from torch and ONNX Runtime are similar.'''			)
														logger.info('''Success.'''			)


def    _snake_case  (	)		->       List[str]:
							lowerCAmelCase__			  =      parse_args()
							lowerCAmelCase__			  =      5
							lowerCAmelCase__			  =      4

							# Make one log on every process with the configuration for debugging.
							logging.basicConfig(
							    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''					,       datefmt='''%m/%d/%Y %H:%M:%S'''					,       level=logging.INFO					,       )

							logger.setLevel(logging.INFO			)
							transformers.utils.logging.set_verbosity_error()

							lowerCAmelCase__			  =      torch.device(args.device			)

							lowerCAmelCase__  ,   lowerCAmelCase__			  =      load_model_tokenizer(args.model_name_or_path					,       A			)

							if model.config.decoder_start_token_id is None:
														raise ValueError('''Make sure that `config.decoder_start_token_id` is correctly defined'''			)

							model.to(A			)

							if args.max_length:
														lowerCAmelCase__			  =      args.max_length

							if args.num_beams:
														lowerCAmelCase__			  =      args.num_beams

							if args.output_file_path:
														lowerCAmelCase__			  =      args.output_file_path
							else:
														lowerCAmelCase__			  =      '''BART.onnx'''

							logger.info('''Exporting model to ONNX'''			)
							export_and_validate_model(A					,       A					,       A					,       A					,       A			)


if __name__ == "__main__":
		main()



"""simple docstring"""



import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch
def      UpperCAmelCase     (       _lowercase :   Union[str, Any]	)						-> Union[str, Any]:


	"""simple docstring"""




	lowerCAmelCase_      							=			os.path.join(args.tf_model_dir			,   '''parameters.json'''	)
	lowerCAmelCase_      							=			json.loads(open(_lowercase	).read()	)
	if not params:
		raise ValueError(
		    F"""It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file."""	)
	if not args.output.endswith('''.pt'''	):
		lowerCAmelCase_      							=			args.output + '''.pt'''
	lowerCAmelCase_      							=			OrderedDict()
	with tf.device('''/CPU:0'''	):
		lowerCAmelCase_      							=			tf.train.load_checkpoint(args.tf_model_dir	)
		lowerCAmelCase_      							=			reader.get_variable_to_shape_map()
		for key_name in shapes.keys():
			lowerCAmelCase_      							=			reader.get_tensor(_lowercase	).astype(np.floataa	)
			if key_name.endswith('''/adam_m'''	) or key_name.endswith('''/adam_v'''	):
				continue
			if key_name.startswith('''pasts/'''	):
				if key_name.startswith('''pasts/mlp'''	):
					lowerCAmelCase_      							=			int(key_name[9]	)
				elif key_name.startswith('''pasts/out'''	):
					lowerCAmelCase_      							=			8
				lowerCAmelCase_      							=			'''model.sqout.%d.weight''' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
				lowerCAmelCase_      							=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
				lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif key_name.startswith('''model/moe'''	):
				lowerCAmelCase_      							=			int(key_name[9:].split('''/'''	)[0]	)
				if key_name.endswith('''/switch_gating/kernel'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.mlp.router.classifier.weight''' % player
					lowerCAmelCase_      							=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				elif key_name.endswith('''/softmlp/kernel'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.soft_bypass_mlp.weight''' % player
					lowerCAmelCase_      							=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				elif key_name.endswith('''/wo/kernel'''	) or key_name.endswith('''/wi/kernel'''	):
					lowerCAmelCase_      							=			key_name[-9:-7]
					for i in range(1_6	):
						lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight''' % (player, i, nlayer)
						lowerCAmelCase_      							=			(
						    vnp[i].transpose([1, 0]	).copy()
						)  # In Mesh-Tensorflow, it is one array, so it is divided
						lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif key_name.startswith('''model/mlp'''	):
				lowerCAmelCase_      							=			int(key_name[9:].split('''/'''	)[0]	)
				if key_name.endswith('''/p1/kernel'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.mlp.wi.weight''' % player
					lowerCAmelCase_      							=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				elif key_name.endswith('''/p1/bias'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.mlp.wi.bias''' % player
					lowerCAmelCase_      							=			vnp.copy()  # same because it is one dimensional
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				elif key_name.endswith('''/p2/kernel'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.mlp.wo.weight''' % player
					lowerCAmelCase_      							=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				elif key_name.endswith('''/p2/bias'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.mlp.wo.bias''' % player
					lowerCAmelCase_      							=			vnp.copy()  # same because it is one dimensional
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif key_name.startswith('''model/ln'''	):
				lowerCAmelCase_      							=			int(key_name[8:].split('''/'''	)[0]	)
				if key_name.endswith('''/b'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.norm.bias''' % player
					lowerCAmelCase_      							=			vnp.copy()  # same because it is one dimensional
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				elif key_name.endswith('''/g'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.norm.weight''' % player
					lowerCAmelCase_      							=			vnp.copy()  # same because it is one dimensional
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif key_name.startswith('''model/att'''	):
				lowerCAmelCase_      							=			int(key_name[9:].split('''/'''	)[0]	)
				if key_name.endswith('''/qkv/kernel'''	):
					lowerCAmelCase_      							=			vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
					lowerCAmelCase_      							=			state[:, 0, :, :]
					lowerCAmelCase_      							=			state[:, 1, :, :]
					lowerCAmelCase_      							=			state[:, 2, :, :]
					lowerCAmelCase_      							=			(
					    state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]	)
					    .transpose([1, 0]	)
					    .copy()
					)  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase_      							=			(
					    state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]	)
					    .transpose([1, 0]	)
					    .copy()
					)  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase_      							=			(
					    state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]	)
					    .transpose([1, 0]	)
					    .copy()
					)  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase_      							=			'''model.blocks.%d.self_attn.self_attn.q_proj.weight''' % player
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
					lowerCAmelCase_      							=			'''model.blocks.%d.self_attn.self_attn.k_proj.weight''' % player
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
					lowerCAmelCase_      							=			'''model.blocks.%d.self_attn.self_attn.v_proj.weight''' % player
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				elif key_name.endswith('''/o/kernel'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.self_attn.self_attn.out_proj.weight''' % player
					lowerCAmelCase_      							=			(
					    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]	).transpose([1, 0]	).copy()
					)  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif key_name.startswith('''model/an'''	):
				lowerCAmelCase_      							=			int(key_name[8:].split('''/'''	)[0]	)
				if key_name.endswith('''/b'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.self_attn.norm.bias''' % player
					lowerCAmelCase_      							=			vnp.copy()  # same because it is one dimensional
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				elif key_name.endswith('''/g'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.self_attn.norm.weight''' % player
					lowerCAmelCase_      							=			vnp.copy()  # same because it is one dimensional
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif (
			    key_name.startswith('''model/wte'''	)
			    or key_name.startswith('''model/wpe'''	)
			    or key_name.startswith('''model/ete'''	)
			):
				lowerCAmelCase_      							=			{'''wte''': '''embed_tokens''', '''wpe''': '''position_embeddings''', '''ete''': '''extra_position_embeddings'''}[
				    key_name[-3:]
				]
				lowerCAmelCase_      							=			'''model.%s.weight''' % nlayer
				lowerCAmelCase_      							=			vnp.copy()  # same in embedded
				lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				if key_name.startswith('''model/wte'''	):
					lowerCAmelCase_      							=			'''lm_head.weight'''
					lowerCAmelCase_      							=			vnp.copy()  # same in embedded
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif key_name.startswith('''model/wob'''	):
				lowerCAmelCase_      							=			'''final_logits_bias'''
				lowerCAmelCase_      							=			vnp.copy()  # same in embedded
				lowerCAmelCase_      							=			state.reshape((1, -1)	)
				lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif key_name == "model/dense/kernel":
				lowerCAmelCase_      							=			'''model.last_project.weight'''
				lowerCAmelCase_      							=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
				lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif key_name == "model/dense_1/bias":
				lowerCAmelCase_      							=			'''model.last_project.bias'''
				lowerCAmelCase_      							=			vnp.copy()  # same because it is one dimensional
				lowerCAmelCase_      							=			torch.tensor(_lowercase	)
	torch.save(_lowercase			,   args.output	)


if __name__ == "__main__":
		lowercase_							=							argparse.ArgumentParser(
		    description='model converter.', formatter_class=argparse.ArgumentDefaultsHelpFormatter
		)
		parser.add_argument('--tf_model_dir', metavar='PATH', type=str, required=True, help='import model')
		parser.add_argument('--output', metavar='PATH', type=str, required=True, help='output model')
		lowercase_							=							parser.parse_args()
		convert_tf_gptsan_to_pt(args)